ALGEBRAIC COMBINATORICS Damir Yeliussizov Random plane partitions and corner distributions Volume 4, issue 4 (2021), p. 599-617. <http://alco.centre-mersenne.org/item/ALCO_2021__4_4_599_0> © The journal and the authors, 2021. Some rights reserved. This article is licensed under the CREATIVE COMMONS ATTRIBUTION 4.0 INTERNATIONAL LICENSE. http://creativecommons.org/licenses/by/4.0/ Access to articles published by the journal Algebraic Combinatorics on the website http://alco.centre-mersenne.org/ implies agreement with the Terms of Use (http://alco.centre-mersenne.org/legal/). Algebraic Combinatorics is member of the Centre Mersenne for Open Scientific Publishing www.centre-mersenne.org Algebraic Combinatorics Volume 4, issue 4 (2021), p. 599–617 https://doi.org/10.5802/alco.171 Random plane partitions and corner distributions Damir Yeliussizov Abstract We explore some probabilistic applications arising in connections with K-theoretic symmetric functions. For instance, we determine certain corner distributions of random lozenge tilings and plane partitions. We also introduce some distributions that are naturally related to the corner growth model. Our main tools are dual symmetric Grothendieck polynomials and normalized Schur functions. 1. Introduction Combinatorics arising in connection with K-theoretic Schubert calculus is quite rich. Accompanied by certain families of symmetric functions, it usually presents some in- homogeneous deformations of objects beyond classical Schur (or Schubert) case. While the subject is intensively studied from combinatorial, algebraic and geometric aspects, see [20,7,8, 34, 19, 13, 35] and many references therein, much less is known about probabilistic connections (unlike interactions between probability and representation theory). Some work in this direction was done in [32, 22] and related problems were addressed in [38].(1) In this paper, we give several probabilistic applications obtained with tools from combinatorial K-theory. We mostly focus on one deformation of Schur functions, the dual Grothendieck polynomials, whose associated combinatorics is fairly neat. In particular, we show that these functions are naturally related to the corner growth model [14, 15, 28, 27] (which can also be viewed as a totally asymmetric simple exclusion process or a directed last passage percolation), see Subsec. 1.6 and Sec.8. We are now going to discuss our results across a few related models. 1.1. N-matrices with bounded last passage time. A lattice path Π with ver- tices indexed by N2 (where N = {0, 1, 2,...}) is called a monotone path if it uses only steps of the form (i, j) → (i + 1, j), (i, j + 1). An N-matrix is a matrix of nonnegative integers with only finitely many nonzero entries. Let a, b, c be positive integers. Given an N-matrix D = (dij) with b rows and c columns, the last passage time G(b, c) is defined as X G(b, c) = max dij, Π (i,j)∈Π Manuscript received 26th May 2020, revised 30th January 2021, accepted 3rd March 2021. Keywords. Random plane partitions, lozenge tilings, dual Grothendieck polynomials. (1)See also Remark 8.6 on recent works. ISSN: 2589-5486 http://algebraic-combinatorics.org/ Damir Yeliussizov where the maximum is taken over monotone paths Π from (1, 1) to (b, c). Let BM(a, b, c) be the set of b×c N-matrices whose last passage time is bounded by a, i.e. n b,c o BM(a, b, c) := D = (dij)i,j=1 : G(b, c) 6 a, dij ∈ N . This set is in fact equinumerous with boxed plane partitions (see Theorem 2.2). Consider the uniform probability measure on BM(a, b, c). For a random matrix D ∈ BM(a, b, c), define the column marginals b X C` = di` i=1 i.e. as the sum of entries in `-th column of D where ` ∈ [1, c]. It is natural to ask what are distributions of C`. We determine these distributions. The results about C` are presented via lozenge tilings (see Theorem 1.3) below. We show that the random variables (C`) are exchangeable (see Theorem 1.2; in particular, Ci and Cj have the same distribution for all i, j ∈ [1, c]) and obtain limiting (joint) distributions with different asymptotic regimes for the parameters a, b, c. In some regimes, the variables C` become asymptotically independent. By symmetry, we also get similar results for row marginals. 1.2. Boxed plane partitions. A plane partition is an N-matrix π = (πij)i,j>1 satisfying πij > πi+1,j, πij > πi,j+1, i, j > 1. By default we ignore zeros of π. We denote by sh(π) := {(i, j): πij > 0} the shape of π. Let PP(a, b, c) be the set of boxed plane partitions that fit inside the box a × b × c, i.e. the first row of the shape is at most a, the first column is at most b, and the first entry is at most c. We show that | PP(a, b, c)| = | BM(a, b, c)| (Theorem 2.2). Consider the uniform probability measure on the set PP(a, b, c). Recall that classi- cal MacMahon’s theorem on boxed plane partitions gives the explicit product formula a b c Y Y Y i + j + k − 1 Z := | PP(a, b, c)| = . abc i + j + k − 2 i=1 j=1 k=1 Note that we also have b+c Zabc = s(ab)(1 ), n where sλ is the Schur polynomial and 1 := (1,..., 1) repeated n times. (See e.g. [31, Ch. 7].) For a uniformly random plane partition π ∈ PP(a, b, c), let X` be the number of columns of π that contain entry ` ∈ [1, c]. In fact, the variables X` become image of the marginals C` described previously under the bijection which we describe in Sec.2. Similarly, (X`) are exchangeable, and Xi and Xj have the same distribution for all i, j ∈ [1, c]. Theorem 1.3 below (stated in terms of lozenge tilings) gives limiting distributions in several asymptotic regimes. By symmetry, we also get similar results for the random variables Y`, the number of rows containing ` in a random plane partition. Hence the results also imply some bounds for the area containing `. 1.3. Lozenge tilings of a hexagon. A lozenge tiling is a tiling of a planar domain with three types of lozenges which we refer to as left, top and right tiles. Let LT(a, b, c) be the set of lozenge tilings of a hexagon with sides (a, b, c, a, b, c) as in Fig.1. For a lozenge tiling T ∈ LT(a, b, c), define: Algebraic Combinatorics, Vol. 4 #4 (2021) 600 Random plane partitions and corner distributions • left corners (or simply corners) of T , that are local tile configurations of the form • for each corner α, its height h(α) ∈ [1, c] is the z-coordinate of the top tile of α, when T is viewed in R3 as a pile of cubes (boxed plane partition), see Fig.1. z 3 3 2 2 2 c 3 2 2 2 2 2 1 1 1 1 a b Figure 1. A plane partition and the corresponding lozenge tiling of a hexagon with sides a = 5, b = 4, c = 3. It has 11 left corners, 5 of which have height 2. Consider the uniform probability measure on the set LT(a, b, c). We clearly have | LT(a, b, c)| = | PP(a, b, c)|, where the bijection is given by writing the height of each top tile. The random variables C` and X` defined for previous models translate into the following random variables for lozenge tilings. Definition 1.1 (Corner distributions). Let Γ` be the number of left corners of height `. We show the following symmetry. Theorem 1.2 (Exchangeability of levels, cf. Corollary 5.2). The random variables (Γ`) are exchangeable. In particular, Γi and Γj have the same distribution for all i, j ∈ [1, c]. We find limits of corner distributions Γ in several asymptotic regimes. Theorem 1.3. We have convergence in distribution in the following regimes: (i) Poisson: for a, b, c → ∞ with ab/c → t > 0 we have Γ` → Poisson(t). (ii) Negative binomial: for b fixed and a, c → ∞ with a/(a + c) → q ∈ (0, 1) we have Γ` → NB(b, q). (iii) Gaussian: for a, b, c → ∞ with a/(b + c) → u > 0 and b/(b + c) → q ∈ (0, 1) we have Γ − µN ` √ → N (0, 1), σ N where N = b + c, µ = uq, and σ = pu(1 + u)q(1 − q). Moreover, the joint distribution of any collection of random variables Γ` weakly converges to the distribution of independent random variables given in corresponding regimes (i), (ii) or (iii). Algebraic Combinatorics, Vol. 4 #4 (2021) 601 Damir Yeliussizov Consider the Gaussian regime, say for a = b = c = n → ∞. It is known that (with a probability going to 1) random lozenge tilings have the arctic circle phenomenon: outside of the inscribed circle of the hexagon, there are frozen regions, and inside there is a liquid region [10,9, 16, 18], see Fig.2. Note that left corners on the topmost level at height c form a profile for the upper frozen√ region. Then the number of such corners is about n/2 with fluctuations of order n, which is compatible with known edge be- havior. However what may seem surprising is the behavior across height levels. There is a kind of invariance there, the number of corners still has the same distribution on each level (even though some are inside the liquid region). Moreover, the theorem also tells us that there is asymptotic independence between height levels which is different from the situation between slices parallel to the sides of the hexagon.